Muon
Muon at Beyond Market Intelligence is a file of 2 stories. The newest of them: “Tauon brings faster training and lower loss to AI optimization” and “Why Adam Misses What Gradient Descent Naturally Preserves”. A new optimizer called Tauon is turning heads in the r/MachineLearning community, and for good reason. The loss landscape of factored models hides a subtle trap, and this work exposes it with refreshing clarity. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Muon story on Beyond Market Intelligence, newest first.
Tauon brings faster training and lower loss to AI optimization
A new optimizer called Tauon is turning heads in the r/MachineLearning community, and for good reason. It promises faster training and lower loss by rethinking how we handle polynomials and orthogonalization, building on ideas from Muon but streamlining the process. The initial results are compelling: Tauon hit a lower validation loss than both Muon and AdamW, all while running nearly as fast as the baseline. It's a small-scale test, but the stability and compute efficiency suggest real potential.
Why Adam Misses What Gradient Descent Naturally Preserves
The loss landscape of factored models hides a subtle trap, and this work exposes it with refreshing clarity. Adam's per-coordinate scaling breaks rotation invariance, a property GD naturally respects, and that single flaw explains why it loses low-rank bias. The one-parameter transition to a shared scalar is a clever mechanism, proving anisotropy, not adaptivity, is the culprit. Muon's crossover near 4% tail energy is particularly striking, showing both claimed behaviors along one axis.